Display system, display method, and non-transitory computer-readable recording medium recording display program

ABSTRACT

A display system is provided in production equipment that has driving means and monitoring means, having controllable features. The display system includes a control unit, a display unit, a storage unit, and an input unit. The storage unit stores: the features output over time from one or more of the driving means and the monitoring means; and causal relationship model data in which one or more causal factors of one or more abnormalities that can occur in the production equipment are selected from among the driving means and the monitoring means and expressed as a causal relationship model in association with a relationship between the causal factors. The control unit displays, on the display unit, the causal factors of the individual abnormalities, the one or more features corresponding to the causal factors, and changes over time in the features.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a 371 application of the International PCTapplication serial no. PCT/JP2020/003869, filed on Feb. 3, 2020, whichclaims the priority benefits of Japan Patent Application No.2019-045645, filed on Mar. 13, 2019. The entirety of each of theabove-mentioned patent applications is hereby incorporated by referenceherein and made a part of this specification.

BACKGROUND Technical Field

The present invention relates to a display system, a display method, anda non-transitory computer-readable recording medium recording a displayprogram.

Description of Related Art

As a method for monitoring a state of equipment, Patent Document 1 hasproposed a method in which modes are divided for each operation statebased on an event signal, a normal model is created for each mode, andan abnormality is determined based on the created normal model. In thismethod, a false alarm in which normality is incorrectly determined as anabnormality is prevented from occurring by checking the sufficiency oflearning data used to create the normal model and setting a thresholdvalue to be used for determination of an abnormality based on the resultof the checking.

In addition, Patent Document 2 has proposed a method for detecting theoccurrence of an abnormality of a product produced by productionequipment. Specifically, Patent Document 2 has proposed a method inwhich data collected from a production system is classified for a normalproduct case and an abnormal product case, a feature at which asignificant difference is made between the normal product case and theabnormal product case is identified, and whether a product is normal isdiagnosed based on the identified feature.

RELATED ART Patent Document

[Patent Document 1]

-   Japanese Patent Laid-Open No. 2015-172945A    [Patent Document 1]-   Japanese Patent Laid-Open No. 2010-277199A

SUMMARY Technical Problem

However, even though an abnormality should be instantly resolved when itoccurs in production equipment, a user generally examines a manual, orthe like for the cause of the abnormality, then checks the state of theproduction equipment at the time of the occurrence of the abnormality,and then performs processing to resolve the abnormality. However, ittakes time to examine the manual each time an abnormality occurs, whichmay delay processing. The present invention has been conceived to solvethe above-described problem, and provides a display system, a displaymethod, and a display program that enable a state of productionequipment to be easily checked when an abnormality occurs.

Solution to the Problem

A display system according to the present invention is a display systemprovided in production equipment that produces a product and has one ormore driving means for driving the production equipment and one or moremonitoring means for monitoring the production, in which the drivingmeans and the monitoring means have one or more controllable features,the display system including a control unit, a display unit, a storageunit, and an input unit, in which the storage unit stores the featuresoutput over time from one or more of the driving means and themonitoring means and causal relationship model data in which one or morecausal factors of one or more abnormalities that can occur in theproduction equipment are selected from among the driving means and themonitoring means and expressed as a causal relationship model inassociation with a relationship between the causal factors, and thecontrol unit displays, on the display unit, the causal factor of theindividual abnormalities, the one or more features corresponding to thecausal factor, and changes over time in the features.

According to this configuration, when an abnormality occurs, it isconfigured that a causal factor thereof, a feature corresponding to thecausal factor, and changes over time in the feature are displayed on thedisplay unit, and thus a user can easily check the state of theproduction equipment when an abnormality occurs, or a transition of theabnormality, for example, by viewing the changes over time in thefeature.

In the display system, the control unit may display, on the displayunit, the causal factor of the individual abnormalities and the one ormore features corresponding to the causal factor as a list, and theinput unit may receive a selection of one of the features from the list,and the control unit may display, on the display unit, changes over timein the selected feature.

According to this configuration, the causal factor of the abnormalityand the features corresponding thereto are displayed on the display unitas a list, and changes over time in a feature selected therefrom can bevisually recognized. For example, controllable features may differdepending on abnormalities even for the same causal factor, and thus itis easy to visually recognize changes over time in the features byselecting the features from the list.

In the display system, the storage unit may store causal relationshipmodel data related to a plurality of the abnormalities, and the inputunit may receive a selection of one abnormality from the plurality ofabnormalities, and the control unit may display, on the display unit, alist corresponding to the selected abnormality.

According to this configuration, the causal relationship model datarelated to the plurality of abnormalities is stored, and thus a user canselect an abnormality that has actually occurred from the plurality ofabnormalities with the input unit. Therefore, the list and changes overtime in the features included in the list can be easily recognized foreach abnormality that has occurred.

In the display system, the input unit may receive a selection of apredetermined time, and the control unit may display, on the displayunit, changes over time in the features for the selected predeterminedtime.

According to this configuration, a user can select a predetermined timefor the changes over time in the features, and thus can intensivelycheck the changes over time in the features for a time at which theoccurrence of an abnormality can be caused. Therefore, a work time canbe shortened without checking the changes in the features at all times.

A display method according to the present invention is a display methodfor displaying, on a display unit, a state of production equipment thatproduces a product and has one or more driving means for driving theproduction equipment and one or more monitoring means for monitoring theproduction, in which the driving means and the monitoring means have oneor more controllable features, the display method including acquiringthe features output over time from one or more of the driving means andthe monitoring means, storing causal relationship model data in whichone or more causal factors of one or more abnormalities that can occurin the production equipment are selected from among the driving meansand the monitoring means and expressed as a causal relationship model inassociation with a relationship between the causal factors, anddisplaying, on the display unit, the causal factor of the individualabnormalities, the one or more features corresponding to the causalfactor, and changes over time in the features.

A non-transitory computer-readable recording medium recording a displayprogram according to the present invention records a display program fordisplaying, on a display unit, a state of production equipment thatproduces a product and has one or more driving means for driving theproduction equipment and one or more monitoring means for monitoring theproduction, in which the driving means and the monitoring means have oneor more controllable features, the display program causing a computer toexecute acquiring the features output over time from one or more of thedriving means and the monitoring means, storing causal relationshipmodel data in which one or more causal factors of one or moreabnormalities that can occur in the production equipment are selectedfrom among the driving means and the monitoring means and expressed as acausal relationship model in association with a relationship between thecausal factors, and displaying, on the display unit, the causal factorof the individual abnormalities, the one or more features correspondingto the causal factor, and changes over time in the features.

Effects

According to the present invention, a state of production equipment whenan abnormality occurs can be easily checked.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an example of a situation to which thepresent invention is applied.

FIG. 2 is a block diagram illustrating a hardware configuration of ananalysis device according to an embodiment of the present invention.

FIG. 3 is a schematic diagram of production equipment according to anembodiment of the present invention.

FIG. 4 is a block diagram illustrating a functional configuration of theanalysis device.

FIG. 5 is a flowchart showing an example of construction of a causalrelationship model.

FIG. 6 illustrates an example of a relationship between a control signaland takt times.

FIG. 7A illustrates an example of the causal relationship model.

FIG. 7B illustrates an example of the causal relationship model.

FIG. 7C illustrates an example of the causal relationship model.

FIG. 8 is a diagram in which nodes of the causal relationship model areoverlaid on a schematic diagram of a packaging machine.

FIG. 9A illustrates an example of a screen of a display device.

FIG. 9B illustrates an example of the screen of the display device.

FIG. 9C illustrates an example of the screen of the display device.

DESCRIPTION OF THE EMBODIMENTS

An embodiment according to an aspect of the present invention (whichwill also be referred to as “the present embodiment”) will be describedbased on the drawings. However, the present embodiment which will bedescribed below is merely an example of the present invention in everyrespect. It is needless to say that various improvements andmodifications can be performed without departing from the scope of thepresent invention. In other words, a specific configuration according tothe embodiment may be appropriately employed to implement the presentinvention. Further, although data appearing in the present embodiment isdescribed using a natural language, and more specifically, specified ina pseudo language, a command, a parameter, a machine language, and thelike that can be recognized by a computer.

1. Application Example

First, an example of a situation to which the present invention isapplied will be described with reference to FIG. 1. FIG. 1 schematicallyillustrates an example of a situation to which a production systemaccording to the present embodiment is applied. The production systemaccording to the present embodiment includes a packaging machine 3 thatis an example of production equipment, an analysis device 1, and adisplay device 2. The analysis device 1 is a computer configured toderive and display a causal relationship between a servo motor (drivingmeans) provided in the packaging machine 3 and various sensors(monitoring means). Further, the driving means such as the servo motorand the monitoring means such as the various sensors will becollectively referred to as mechanisms below. In addition, a causalfactor according to the present invention corresponds to a mechanism,among the mechanisms, which causes the occurrence of an abnormality.

The analysis device 1 generates a causal relationship model for themechanisms with respect to an abnormality that can occur in thepackaging machine 3 and displays the causal relationship model on ascreen 21 of the display device 2. The example of FIG. 1 illustrates acausal relationship model when abnormal wear of a leather belt for thebrake of a film roll 30 (see FIG. 3), which will be described below,takes place. In other words, servos 1, 3, and 4 among a plurality ofservo motors provided in the packaging machine 3 are displayed as nodes,and the servos are linked by edges. In addition, an orientation of anedge indicates a causal relationship. That is, the edges indicate that,when wear of a leather belt takes place, the servo 1 affects the servo3, further the servo 3 affects the servo 4, and as a result, wear of theleather belt takes place. Thus, an operator of the packaging machine 3is only required to check the servo 4, 3, and 1 in this order for thecause of an abnormality. However, although details will be describedbelow, each of the servo motors has a plurality of controllable featuressuch as torque, location, and the like, and any of the features of theservo motors establishes the causal relationship.

In addition, the display device 2 displays a schematic diagram of thepackaging machine 3, and the causal relationship model is overlaid anddisplayed on the schematic diagram as illustrated in the example ofFIG. 1. At this time, each node of the causal relationship model isdisposed at the position at which each servo motor is provided in theschematic diagram of the packaging machine 3. Thus, an operator canidentify a mechanism that is the cause of an abnormality while viewingthe schematic diagram. Thus, it is possible to visually recognize themechanism of the packaging machine 3 to be checked to return theabnormality to normality with ease.

Furthermore, the screen 21 displays a list showing each mechanism and afeature thereof and a graph showing a change over time in a featureselected from the list. Thus, when a feature of any mechanism that isthe cause in a causal relationship is selected, a change over timethereof is displayed, and thus a user can see the change over time tocheck the time point at which the abnormality occurred.

Further, although the packaging machine 3 is shown as an example ofproduction equipment in the above description, equipment that canproduce any product is applicable, and a type thereof is notparticularly limited. A type of each mechanism may not be particularlylimited, and may be appropriately selected depending on an embodiment.Each mechanism may be, for example, a conveyor, a robot arm, a servomotor, a cylinder (molding machine, or the like), a suction pad, acutting device, a sealing device, or the like. In addition, eachmechanism may be a complex device, for example, a printing machine, amounting machine, a reflow furnace, a substrate inspection device, andthe like, in addition to the above-described packaging machine 3.Furthermore, each mechanism may include, for example, in addition to adevice involved with any physical operation described above, a devicethat detects any information using various sensors, a device thatacquires data from various sensors, a device that detects anyinformation from acquired data, and a device that performs internalprocessing such as a device that processes acquired data forinformation. One mechanism may be constituted by one or a plurality ofdevices, or configured as a part of a device. One device may beconstituted by a plurality of mechanisms. In addition, in a case inwhich the same device executes a plurality of processes, each partthereof may be regarded as a separate mechanism. When the same deviceexecutes a first process and a second process, for example, the devicethat executes the first process may be regarded as a first mechanism,and the device that executes the second process may be regarded as asecond mechanism.

2. Configuration Example

<2-1. Hardware Configuration>

Next, an example of a hardware configuration of a production systemaccording to the present embodiment will be described. FIG. 2 is a blockdiagram illustrating an example of a hardware configuration of theanalysis device 1 according to the present embodiment, and FIG. 3 is adiagram illustrating a schematic configuration of the packaging machine.

<2-1-1. Analysis Device>

First, the example of the hardware configuration of the analysis device1 according to the present embodiment will be described using FIG. 2.The analysis device 1 is a computer in which a control unit 11, astorage unit 12, a communication interface 13, an external interface 14,an input device 15, and a drive 16 are electrically connected asillustrated in FIG. 2.

The control unit 11 includes a central processing unit (CPU), a randomaccess memory (RAM), a read only memory (ROM), and the like and controlseach of constituent elements in accordance with information processing.The storage unit 12 is an auxiliary storage device, for example, a harddisk drive, a solid state drive, or the like, and stores a program 121executed by the control unit 11, schematic diagram data 122, causalrelationship model data 123, operation state data 124, and the like.

The program 121 is a program for generating a causal relationship modelfor a mechanism with respect to an abnormality occurring in thepackaging machine 3, displaying the causal relationship model on thedisplay device 2, and the like. The schematic diagram data 122 is datashowing a schematic diagram of target production equipment, and datashowing a schematic diagram of the packaging machine 3 in the presentembodiment. The schematic diagram may be a schematic diagram of thewhole packaging machine that helps at least the position of a mechanismindicated in the causal relationship model to be recognized, and may notbe necessarily a detailed diagram. In addition, an enlarged diagramillustrating only a part of the packaging machine 3 may be employed.

The causal relationship model data 123 is data indicating a causalrelationship model for the occurrence of an abnormality constructed witha feature of each of the mechanisms extracted from the packaging machine3. That is, it is data indicating a causal relationship betweenmechanisms when an abnormality occurs. Although the causal relationshipmodel data is generated with features extracted from the packagingmachine 3, and the like in the analysis device 1 as will be describedbelow, causal relationship model data generated in advance by anexternal device may be stored.

The operation state data 124 is data indicating an operation state ofthe packaging machine 3. Although details thereof will be describedbelow, data that can be generated in driving each of the mechanismsdescribed below, for example, measurement data, for example, torque,speed, acceleration, temperature, pressure, and the like can beemployed. In addition, in a case in which a mechanism is a sensor, adetection result may be, for example, detection data indicating whetherthere is content WA by “ON” or “OFF.”

The communication interface 13 is an interface, for example, a wiredlocal area network (LAN) module, a wireless LAN module, or the like forperforming wired or wireless communication. That is, the communicationinterface 13 is an example of a communication unit configured to performcommunication with another device. The analysis device 1 according tothe present embodiment is connected to the packaging machine 3 via thecommunication interface 13.

The external interface 14 is an interface for connecting to an externaldevice and is appropriately configured in accordance with an externaldevice to be connected. In the present embodiment, the externalinterface 14 is connected to the display device 2. Further, a knownliquid crystal display, touch panel display, or the like may be used forthe display device 2.

The input device 15 is a device for input, for example, a mouse, akeyboard, and the like.

The drive 16 is, for example, a compact disk (CD) drive, a digitalversatile disk (DVD) drive, or the like, and is a device for reading aprogram stored in a storage medium 17. A type of the drive 16 may beappropriately selected in accordance with the type of storage medium 17.Further, at least some of the various kinds of data 122 to 124 includingthe program 121 stored in the storage unit may be stored in the storagemedium 17.

The storage medium 17 is a medium in which information such as arecorded program is accumulated by an electrical, magnetic, optical,mechanical, or chemical action so that a computer, other devices,machines, or the like can read the information such as the program. Asan example of the storage medium 17, a disc-type storage medium such asa CD or a DVD is illustrated in FIG. 2. However, a type of the storagemedium 17 is not limited to a disc type, and may be a type other thanthe disc type. An example of a storage medium of a type other than thedisc type may include a semiconductor memory, for example, a flashmemory, or the like.

Further, regarding the specific hardware configuration of the analysisdevice 1, constituent components can be omitted, replaced, and added asappropriate in accordance with an embodiment. For example, the controlunit 11 may include a plurality of processors. The analysis device 1 maybe constituted by a plurality of information processing devices. Inaddition, for the analysis device 1, a generic server device, or thelike may be used in addition to an information processing devicedesigned exclusively for a service to be provided.

<2-1-2. Packaging Machine>

Next, an example of a hardware configuration of the packaging machine 3according to the present embodiment will be described using FIG. 3. FIG.3 schematically illustrates an example of a hardware configuration ofthe packaging machine 3 according to the present embodiment. Thepackaging machine 3 is a so-called horizontal pillow packaging machinewhich is a device for packaging content WA such as food (dried noodles,etc.) or stationery (erasers, etc.). However, the type of content WA canbe appropriately selected in accordance with an embodiment and is notparticularly limited. The packaging machine 3 includes a film roll 30 onwhich a packaging film is wound, a film transport part 31 thattransports the packaging film, a content transport part 32 thattransports content WA, and a packing part 33 that packages content withthe packaging film.

The packaging film may be a resin film, for example, a polyethylenefilm, or the like. The film roll 30 has a winding core, and thepackaging film is wound around the winding core. The winding core issupported to be rotatable around the axis, and thus the film roll 30 isconfigured to unwind the packaging film while rotating.

The film transport part 31 includes a drive roller driven by a servomotor (servo 1) 311, a passive roller 312 to which a rotation force isapplied from the drive roller, and a plurality of pulleys 313 thatguides the packaging film while applying tension thereto. With thisconfiguration, the film transport part 31 unwinds the packaging filmfrom the film roll 30 and transports the unwound packaging film to thepacking part 33 without loosening.

The content transport part 32 includes a conveyor 321 that transportsthe content WA to be packaged and a servo motor (servo 2) 322 thatdrives the conveyor 321. The content transport part 32 is connected tothe packing part 33 through the lower part of the film transport part 31as illustrated in FIG. 3. Accordingly, the content WA transported by thecontent transport part 32 is supplied to the packing part 33 andpackaged with the packaging film supplied from the film transport part31. In addition, regarding downstream information of the conveyor 321, afiber sensor (sensor 1) 324 that detects a position of the content WA isprovided. Furthermore, another fiber sensor (sensor 2) 325 that detectsthe placement of the content WA is provided below the conveyor 321.These sensors 1 and 2 detect whether the content WA is being transportedat a correct position to be correctly packaged.

The packing part 33 includes a conveyor 331, a servo motor (servo 3) 332that drives the conveyor 331, a center sealing part 333 that seals thepackaging film in the transport direction, and an end sealing part 334that cuts the packaging film at both ends in the transport direction andseals it at each end.

The conveyor 331 transports the content WA transported from the contenttransport part 32 and the packaging film supplied from the filmtransport part 31. The packaging film supplied from the film transportpart 31 is supplied to the center sealing part 333 while beingappropriately folded such that both side edges in the width directionoverlap. The center sealing part 333 is constituted by, for example, apair of left and right heating rollers (heaters 1 and 2) and seals theboth folded side edges of the packaging film in the transport directionby heating. Accordingly, the packaging film is formed in a tubularshape. The content WA is input to the packaging film formed in thetubular shape. In addition, a fiber sensor (sensor 3) 336 that detects aposition of the content WA is provided above the conveyor 331 on theupstream side of the end sealing part 334.

Meanwhile, the end sealing part 334 has, for example, a roller that isdriven by a servo motor 335, a pair of cutters that open and close inaccordance with rotation of the roller, and heaters (heaters 3) providedat both sides of each of the cutters. Accordingly, the end sealing part334 is configured to cut the tubular packaging film in a directionorthogonal to the transport direction and seal the cut portion byheating. While the tubularly formed packaging film passes through theend sealing part 334, the tip portion of the packaging film is sealed atboth sides in the transport direction and then separated from thesucceeding one, and thereby a package WB containing the content WA isproduced.

<2-1-3. Packaging Step>

The above-described packaging machine 3 can package the content WA inthe following steps. That is, the film transport part 31 unwinds apackaging film from the film roll 30. In addition, the content transportpart 32 transports the content WA to be packaged. Next, the centersealing part 333 of the packing part 33 forms the packaging film thathas been paid out in a tubular shape. Then, after inputting the contentWA into the tubular-shaped packaging film, the end sealing part 334 cutsthe tubular packaging film in the direction orthogonal to the transportdirection and seals the both sides of the cut portion in the transportdirection by heating. Accordingly, the horizontal pillow-shaped packageWB containing the content WA is formed. That is, packaging of thecontent WA is completed.

Further, driving control of the packaging machine 3 can be performed bya PLC, or the like provided separately from the packaging machine 3. Inthis case, the above-described operation state data 124 can be acquiredfrom the PLC. In addition, ten mechanisms to construct a causalrelationship for abnormalities are set in the packaging machine 3configured as described above, as an example. That is, theabove-described servos 1 to 4, heaters 1 to 3, and sensors 1 to 3 areset as mechanisms, and a causal relationship between these mechanismswhen an abnormality occurs is constructed as a causal relationshipmodel. Details thereof will be described below.

<2-2. Functional Configuration>

Next, a functional configuration (software configuration) of theanalysis device 1 will be described. FIG. 4 illustrates an example of afunctional configuration of the analysis device 1 according to thepresent embodiment. The control unit 11 of the analysis device 1 loads aprogram 8 stored in the storage unit 12 in the RAM. Then, the controlunit 11 interprets and executes the program 8 loaded in the RAM usingthe CPU to control each constituent elements. With this configuration,the analysis device 1 according to the present embodiment functions as acomputer including a feature acquisition part 111, a model constructionpart 112, and a display control part 113, as illustrated in FIG. 4.

The feature acquisition part 111 acquires values of a plurality of typesof features calculated from the operation state data 124 indicating anoperation state of the packaging machine 3 for each of a time ofnormality in which the packaging machine 3 forms the package WB normallyand a time of abnormality in which an abnormality occurs in the formedpackage WB. The model construction part 112 selects an effective featurefor predicting an abnormality from the plurality of acquired types offeatures based on a predetermined algorithm with which a degree ofrelationship between each type of feature and an abnormality that occursin the formed package WB is derived from the value of the type offeature for each of the acquired times of normality and abnormality.Then, the causal relationship model 123 indicating a causal relationshipbetween the mechanisms when an abnormality occurs is constructed usingthe selected feature.

The display control part 113 has a function of displaying a schematicdiagram of the packaging machine 3 described above, the causalrelationship model, various features, and the like on the screen 21 ofthe display device 2. In addition, the display control part 113 controlsdisplay of various kinds of information on the screen 21 of the displaydevice 2.

Each function of the analysis device 1 will be described in an operationexample to be described below in detail. Further, in the presentembodiment, an example in which all of the above-described functions arerealized by the generic CPU has been described. However, some or all ofthe above-described functions may be realized by one or a plurality ofdedicated processors. In addition, the functional configuration of theanalysis device 1 may be appropriately subject to an omission, areplacement, and an addition of a function in accordance with anembodiment.

3. Operation Example

Next, an operation example of a production system configured asdescribed above will be described.

<3-1. Creation of Causal Relationship Model>

First, a process procedure for the analysis device to create a causalrelationship model will be described using FIG. 5. FIG. 5 shows anexample of a process procedure for the analysis device to create acausal relationship model.

(Step S101)

In first step S101, the control unit 11 of the analysis device 1functions as the feature acquisition part 111 and acquires values of aplurality of types of features calculated from the operation state data124 indicating an operation state of the packaging machine 3 for each ofa time of normality in which the packaging machine 3 forms the packageWB normally and a time of abnormality in which an abnormality occurs inthe formed package WB.

Specifically, first, the control unit 11 collects the operation statedata 124 for the divided times of normality and abnormality. Although atype of the operation state data 124 to be collected is not particularlylimited as long as it is data indicating a state of the packagingmachine 3, data that can be acquired in drive of each mechanismdescribed above, for example, measured data such as torque, speed,acceleration, temperature, pressure, or the like is employed in thepresent embodiment.

If a mechanism is a sensor, measured data such as an ON time, an OFFtime, a turn-on time, a turn-off time, or the like can be employed asthe operation state data 124. An ON time and an OFF time are a totaltime in which a control signal indicates ON or OFF in a target frame aswill be described in FIG. 6 below, and a turn-on time and a turn-offtime are a time taken for a control signal to turn on or off for thefirst time in a target frame. In addition, the control unit 11 canacquire detection data indicating whether there is content WA with “ON”or “OFF” as a detection result of each sensor, for example, as theoperation state data 124. Further, the collected operation state data124 may be accumulated in the storage unit 12 or in an external storagedevice.

Next, the control unit 11 divides the collected operation state data 124into frames to define a processing range to calculate a feature. Forexample, the control unit 11 may divide the operation state data 124into frames for each fixed time length. However, the packaging machine 3does not necessarily operate at fixed time intervals. Thus, if theoperation state data 124 is divided for each frame of a fixed timelength, it is likely that an operation of the packaging machine 3reflected in each frame deviates.

Thus, the control unit 11 divides the operation state data 124 intoframes for each takt time in the present embodiment. A takt time is atime taken to produce a predetermined number of products, that is, atime taken to form a predetermined number of packaged materials WB. Thetakt time can be specified based on a signal to control the packagingmachine 3, for example, a control signal to control an operation of eachservo motor of the packaging machine 3, or the like.

An example of a relationship between a control signal and takt timeswill be described using FIG. 6. FIG. 6 schematically illustrates anexample of a relationship between a control signal and takt times. Acontrol signal for production equipment that repeats production ofproducts such as the packaging machine 3 is a pulse signal thatperiodically indicate “ON” and “OFF” in accordance with production of apredetermined number of products, as illustrated in FIG. 6.

The control signal illustrated in FIG. 6 indicates, for example, “ON”and “OFF” one time each while one package WB is formed. Thus, thecontrol unit 11 can acquire the control signal from the packagingmachine 3 and set a time from a rise (“ON”) of the acquired signal tothe next rise (“ON”) thereof as a takt time. In addition, the controlunit 11 can divide the operation state data 124 into frames for eachtakt time, as illustrated in FIG. 6.

Further, a type of control signal may not be particularly limited aslong as it is a signal that can be used to control the packaging machine3. For example, in a case in which the packaging machine 3 includes asensor for detecting a mark attached to a packaging film and an outputsignal of the sensor is used to adjust a feeding amount of the packagingfilm, the output signal of the sensor may be used as a control signal.

Next, the control unit 11 calculates a value of a feature from eachframe of the operation state data 124. A type of feature may not beparticularly limited as long as it indicates a characteristic of theproduction equipment.

In a case in which the operation state data 124 is quantitative datasuch as the measured data (physical quantity data of FIG. 6), forexample, the control unit 11 may calculate an in-frame amplitude, amaximum value, a minimum value, a mean value, a variance value, astandard deviation, an autocorrelation coefficient, a maximum value,skewness, or kurtosis of a power spectrum obtained by a Fouriertransform, or the like as a feature.

In addition, in a case in which the operation state data 124 is qualitydata such as the above-described detection data (the pulse data of FIG.6) for example, the control unit 11 may calculate an “ON” time, an “OFF”time, a duty ratio, the number of “ON” times, the number of “OFF” times,or the like of each frame as a feature.

Furthermore, a feature may be derived not only from a single piece ofthe operation state data 124 but also a plurality of pieces of theoperation state data 124. The control unit 11 may calculate, forexample, a mutual correlation coefficient, a ratio, a difference, anamount of synchronization deviation, a distance, or the like betweenframes corresponding to two kinds of the operation state data 124 as afeature.

The control unit 11 calculates a plurality of types of featuresdescribed above from the operation state data 124. Accordingly, thecontrol unit 11 can acquire the values of a plurality of types offeatures calculated from the operation state data 124 for each of a timeof normality and a time of abnormality. Further, a process from thecollection of the operation state data 124 to the calculation of thevalues of the features may be performed by the packaging machine 3 orvarious devices that control the packaging machine, rather than theanalysis device 1. In addition, the control unit 11 discretizes thevalues of the types of the features such that, for example, a state inwhich each value is higher than a threshold value is set to “1” or“high” and a state in which it is lower than the threshold value is setto “0” or “low.”

(Step S102)

In the next step S102, the control unit 11 functions as the modelconstruction part 112 and selects an effective feature for predicting anabnormality from a plurality of acquired types of features based on apredetermined algorithm with which a degree of relationship between eachtype of feature and an abnormality that occurs in the formed package WBis identified from the value of each type of feature acquired in stepS101 for each of the times of normality and abnormality.

The predetermined algorithm may be configured using, for example, aBayesian network. The Bayesian network is one kind of graphical modelingfor expressing a causal relationship between a plurality of randomvariables with a directed acyclic graph structure and expressing thecausal relationship between the random variables with a conditionalprobability.

The control unit 11 can process each acquired feature and a state of thepackage WB as a random variable, that is, can set each acquired featureand a state of the package WB as each node, construct a Bayesiannetwork, and thereby derive a causal relationship between the featureand the state of the package WB. A known method may be used to constructthe Bayesian network. To construct the Bayesian network, a structurallearning algorithm, for example, a greedy search algorithm, a stingysearch algorithm, a full search method, or the like can be used. Inaddition, the Akaike's information criterion (AIC), C4.5,Cooper-Herskovits measure (CHM), minimum description length (MDL),maximum likelihood (ML), or the like can be used as an evaluationcriterion for the constructed Bayesian network. In addition, a pairwisemethod, a wristwise method, or the like can be used as a processingmethod when a missing value is included in learning data (operationstate data 124) to be used to construct the Bayesian network.

For example, FIG. 7A illustrates a causal relationship model when wearof a leather belt is an abnormal event. That is, a causal relationshipmodel in which an average torque and a standard deviation of positionwhich are features of the servo 1 affect an average speed and a maximumtorque which are features of the servo 2 and further affect an averagetorque of the servo 4 is constructed.

FIG. 7B illustrates a causal relationship model when a loose chain ofthe conveyor 321 of the content transport part 32 is an abnormal event.That is, a causal relationship model in which an ON time which is afeature of the sensor 2 affects a turn-on time which is a feature of thesensor 3 and further affects an average torque of the servo 4 isconstructed.

FIG. 7C illustrates a causal relationship model when poor sealing of thepackaging film is an abnormal event. For this abnormal event, a causalrelationship model in which only an average torque of the servo 4 is thecause is constructed. The causal relationship model constructed asdescribed above is stored in the storage unit 12 as the causalrelationship model data 123.

Further, a method for processing each acquired feature and a state ofthe package WB as random variables can be appropriately set inaccordance with an embodiment. A state of the package WB can be regardedas a random variable, for example, by setting an event in which thepackage WB is normal to “0” and an event in which an abnormality occursin the package WB to “1” and associating each of the events with aprobability. In addition, a state of each feature can be regarded as arandom variable, for example, by setting an event in which a value ofeach feature is equal to or less than a threshold value to “0” and anevent in which a value of each feature exceeds the threshold value to“1” and associating each of the events with a probability. However, thenumber of states set for each feature may not be limited to two, and maybe three or more.

<3-2. Display of Causal Relationship Model>

Next, display of the causal relationship model constructed as describedabove will be described. In this case, the control unit 11 of theanalysis device 1 functions as the display control part 113. The displaycontrol part 113 controls display of the screen 21 as described below.First, the display control part 113 displays the schematic diagram 122read from the storage unit 12 with the above-described causalrelationship model 123 overlaid thereon on the screen 21 of the displaydevice 2. FIG. 8 is a diagram in which mechanisms that can be causalfactors of an abnormal event according to the present embodiment areoverlaid on a schematic diagram. Here, the servos 1 to 4, the heaters 1to 3, and the sensors 1 to 3 that are nodes of the causal relationshipmodel are disposed at the installation positions thereof in theschematic diagram as described above. Then, on the screen 21 of thedisplay device 2 to be described next, a mechanism for which the causalrelationship model is constructed is selected as a node from themechanisms, and an edge indicated by an arrow expressing a causalrelationship is displayed as the node in accordance with an abnormalevent selected by a user.

FIG. 9A illustrates an example of the screen 21 of the display device 2showing a causal relationship model. The screen 21 can be operated withthe above-described input device 15. A selection box 211 for selectingan abnormal event is displayed at the upper left side of the screen 21so that an abnormal event can be selected from the pull-down menu. Inthis example, wear of a leather belt, a loose chain, and poor sealingare shown as abnormal events, and wear of a leather belt is selectedfrom them.

An abnormality cause diagram 212 in which a causal relationship model isoverlaid on a schematic diagram of the packaging machine is displayedbelow the selection box 211. In the example of FIG. 9A, the abnormalitycause diagram when wear of the leather belt is an abnormal event isdisplayed. In addition, a list 213 on which mechanisms that are causalfactors and features thereof are shown in accordance with the selectedabnormal event is displayed on a lower left side of the abnormalitycause diagram 212. A user can select any mechanism and feature from thelist 213, and when any one is selected, the mechanism corresponding tothat in the abnormality cause diagram 212 is highlighted. In thisexample, [servo 1: average torque] is selected from the list 213, andthus the servo 1 is highlighted in the abnormality cause diagram 212.Highlight can be made in various methods, and made by means of coloring,blinking, or the like so that the factor can be displayed anddistinguished from other nodes.

Furthermore, changes over time in the selected feature are displayed ina graph 214 on the right side of the list 213. In this example, [servo1: average torque] is selected, and thus the line graph 214 showingchanges over time in the feature is displayed.

FIG. 9B illustrates an example in which the loose chain is displayed asan abnormal event in the box 211. Thus, the mechanism that is the causalfactor of the loose chain and a feature are displayed on the list 213.Here, [servo 4: average torque] is selected, thus the servo 4 ishighlighted in the abnormality cause diagram 212, and the line graph 214showing changes over time in [servo 4: average torque] is displayed.

FIG. 9C illustrates an example in which poor sealing is displayed as anabnormal event in the box 211. Thus, the mechanism that is the causalfactor of the poor sealing and a feature are displayed on the list 213.Here, [servo 4: average torque] is selected, thus the servo 4 ishighlighted in the abnormality cause diagram, and the line graph 214showing changes over time in [servo 4: average torque] is displayed.

The summary of the operation of the screen 21 is as follows. First, auser selects an abnormal event that needs to be checked from theselection box 211 with the input device 15. Then, the display controlpart 113 displays the abnormality cause diagram 212 corresponding to theselected abnormal event and the list 213 on the screen. Then, when anyfeature is selected from the list 213, the corresponding node of theabnormality cause diagram 212 is highlighted, and the graph 214 showingchanges over time in the selected feature is displayed. Thus, the usercan visually recognize the causal relationship related to the abnormalevent while viewing the screen 21. Further, the user can appropriatelyset a period of changes over time in the feature displayed in the graph214.

4. Features

(1) According to the present embodiment, when an abnormality occurs, itis configured that a causal factor thereof, a feature corresponding tothe causal factor, and changes over time in the feature are displayed onthe screen 21, and thus if a threshold value for the occurrence of anabnormality is set for the state of the packaging machine 3 when theabnormality occurs, a transition of the abnormality, or a feature, forexample, a user can easily check at which time point the abnormality hasoccurred by viewing the changes over time in the feature.

(2) According to the present embodiment, the schematic diagram of thepackaging machine 3, the causal relationship model with respect to anabnormality that can occur in the packaging machine 3 are displayed thescreen 21 of the display device 2. At this time, the causal relationshipmodel is overlaid to correspond to the schematic diagram and displayedon the screen 21, and thus the causal factor included in the causalrelationship model can be identified while viewing the schematicdiagram. Thus, it is possible to visually recognize the part of thepackaging machine 3 in which the abnormality has occurred.

(3) Because the causal factor and the feature corresponding thereto foreach abnormal event are displayed as the list 213 on the screen 21, thecausal factor for resolving the abnormality and the feature to controlthe causal factor can be visually recognized. Controllable features maydiffer depending on abnormalities even for the same causal factor, forexample, and thus it is easy to know which is a controllable feature byviewing the list 213.

5. Modified Examples

Although the embodiment of the present invention has been described indetail, the above description is merely an example of the presentinvention in every respect. It is needless to say that variousimprovements and modifications can be performed without departing fromthe scope of the present invention. For example, the following changescan be made. Further, the same reference numerals are used forconstituent elements similar to those of the above-described embodimentbelow, and description of similar points to those of the above-describedembodiment is appropriately omitted. The following modified examples canbe appropriately combined.

<5-1>

Although the selection box 211 for abnormality events, the abnormalitycause diagram 212, the list 213, and the graph 214 are displayed on thescreen 21 in the above-described embodiment, the invention is notlimited thereto, and at least the graph 214 may be displayed. A case inwhich there is one feature depending on target production equipment or atype of an abnormal event is assumable, for example, and thus neitherthe selection box 211 nor the list 213 are necessary in such a case. Inaddition, another type of graph, in addition to the above-described linegraph 214, is applicable to visual recognition of changes over time in afeature as long as it helps visual recognition of changes over time. Inaddition, it is not necessary to display all of the elements 211 to 214on the screen 21, and these may be divided to be displayed on aplurality of screens to allow a user to switch them.

<5-2>

The construction of the causal relationship model introduced in theabove-described embodiment is an example, and another method may beapplied. In addition, the schematic diagram data 122 and the causalrelationship model data 123 constructed by another device can besequentially stored in the storage unit 12.

<5-3>

The invention can also be applied to production equipment other than thepackaging machine 3, and in this case, a mechanism for constructing acausal relationship model can also be appropriately selected inaccordance with the production equipment. In addition, schematic diagramdata related to a plurality of pieces of production equipment can bestored in the storage unit 12 and displayed by the display device 2 foreach corresponding piece of production equipment.

<5-4>

The display system according to the present invention can be constitutedby the analysis device 1 and the display device 2 in the productionsystem. Thus, the display device 2 of the above-described embodimentcorresponds to the display unit of the present invention, and thecontrol unit 11 and the storage unit 12 of the analysis device 1corresponds to the control unit and the storage unit of the presentinvention. The control unit, the storage unit, and the display unit ofthe present invention can be configured by tablet PCs, and the like.

What is claimed is:
 1. A display system provided in production equipmentthat produces a product and has one or more driving means for drivingthe production equipment and one or more monitoring means for monitoringthe production, in which the driving means and the monitoring means haveone or more controllable features, the display system comprising: aprocessor; a display; and a memory, wherein the memory stores: thefeatures output over time from one or more of the driving means and themonitoring means; and causal relationship model data in which aplurality of causal factors of one or more abnormalities that can occurin the production equipment are expressed as a causal relationship modelin association with a causal relationship between the causal factors,and wherein the causal relationship model comprises a plurality of nodesrespectively associated with the plurality of causal factors, the nodesare linked by edges, orientations of the edges indicates the causalrelationship between the causal factors, wherein the processor displays,on the display, a schematic diagram of the production equipment, thecausal relationship model, the causal factor of the individualabnormalities, the one or more features corresponding to the causalfactor, and changes over time in the features, the nodes of the causalrelationship model is overlaid and displayed on the schematic diagram ofthe production equipment, each of the nodes of the causal relationshipmodel indicates the corresponding driving means or the correspondingmonitoring means and is disposed at an installation position of thecorresponding driving means or the corresponding monitoring means in theschematic diagram, and the edges expressing the causal relationship aredisplayed in the schematic diagram along with the nodes.
 2. The displaysystem according to claim 1, further comprising: an input device,wherein the processor displays, on the display, the causal factor of theindividual abnormalities and the one or more features corresponding tothe causal factor as a list, wherein the input device receives aselection of one of the features from the list, and wherein theprocessor displays, on the display, changes over time in the selectedfeature.
 3. The display system according to claim 2, wherein the memorystores causal relationship model data related to a plurality of theabnormalities, wherein the input device receives a selection of oneabnormality from the plurality of abnormalities, and wherein theprocessor displays, on the display, a list corresponding to the selectedabnormality.
 4. The display system according to claim 1, wherein aninput device receives a selection of a predetermined time, and whereinthe processor displays, on the display, changes over time in thefeatures for the selected predetermined time.
 5. A display method fordisplaying, on a display, a state of production equipment that producesa product and has one or more driving means for driving the productionequipment and one or more monitoring means for monitoring theproduction, in which the driving means and the monitoring means have oneor more controllable features, the display method comprising: acquiringthe features output over time from one or more of the driving means andthe monitoring means; storing causal relationship model data in which aplurality of causal factors of one or more abnormalities that can occurin the production equipment are expressed as a causal relationship modelin association with a causal relationship between the causal factors,wherein the causal relationship model comprises a plurality of nodesrespectively associated with the plurality of causal factors, the nodesare linked by edges, orientations of the edges indicates the causalrelationship between the causal factors; and displaying, on the display,a schematic diagram of the production equipment, the causal relationshipmodel, the causal factor of the individual abnormalities, the one ormore features corresponding to the causal factor, and changes over timein the features, the causal relationship model is overlaid and displayedon the schematic diagram of the production equipment, each of the nodesof the causal relationship model indicates the corresponding drivingmeans or the corresponding monitoring means and is disposed at aninstallation position of the corresponding driving means or thecorresponding monitoring means in the schematic diagram, and the edgesexpressing the causal relationship are displayed in the schematicdiagram along with the nodes.
 6. A non-transitory computer-readablerecording medium, recording a display program for displaying, on adisplay, a state of production equipment that produces a product and hasone or more driving means for driving the production equipment and oneor more monitoring means for monitoring the production, in which thedriving means and the monitoring means have one or more controllablefeatures, the display program causing a computer to execute: acquiringthe features output over time from one or more of the driving means andthe monitoring means; storing causal relationship model data in which aplurality of causal factors of one or more abnormalities that can occurin the production equipment are expressed as a causal relationship modelin association with a causal relationship between the causal factors,wherein the causal relationship model comprises a plurality of nodesrespectively associated with the plurality of causal factors, the nodesare linked by edges, orientations of the edges indicates the causalrelationship between the causal factors; and displaying, on the display,a schematic diagram of the production equipment, the causal relationshipmodel, the causal factor of the individual abnormalities, the one ormore features corresponding to the causal factor, and changes over timein the features, the causal relationship model is overlaid and displayedon the schematic diagram of the production equipment, each of the nodesof the causal relationship model indicates the corresponding drivingmeans or the corresponding monitoring means and is disposed at aninstallation position of the corresponding driving means or thecorresponding monitoring means in the schematic diagram, and the edgesexpressing the causal relationship are displayed in the schematicdiagram along with the nodes.